Key entities are individuals or institutions whose opinions or actions are influential. They have the ability to influence the behavior of other entities in the same field or a related field. For example, key opinion leaders, in medicine, are generally medical practitioners or researchers relied upon by other practitioners or researchers to provide informative opinions on standard of care, pharmaceuticals, devices, and the importance of various biochemical pathways among other things. These opinions heavily influence the decisions and behaviors of other practitioners and researchers.
Key opinion leaders in medicine are often targeted by pharmaceutical and biotechnical companies. Key opinion leaders often provide these companies with marketing feedback and advocacy activity. In addition, key opinion leaders sometimes assist pharmaceutical companies in designing and engaging in clinical trials. Key opinion leaders also spread knowledge throughout the medical community on how and when to properly use a drug or device and provide feedback to the companies from the medical community on their products.
Identifying, establishing relationships with, and maintaining relationships with key opinion leaders is time consuming and expensive. It is therefore desirable to efficiently select key opinion leaders which meet the needs of a contacting company. For example, in the field of medicine, it may be more important to select someone who works on a specific subtype of lymphoma as that individual may have more influence over practitioners who deal with patients with that lymphoma subtype. Also, it may be important to choose a key opinion leader who has influence over practitioners practicing in certain geographic regions.
The most common current method used to for select key entities, such as key opinion leaders, is by using surveys. This technique has disadvantages. For instance, surveys tend to be very biased. Individuals who receive surveys tend to select their friends or close colleagues as key opinion leaders rather than making an objective selection. This is due to, at least in part, the fact that survey responders are limited to those individuals willing to fill in the survey in exchange for an honoraria, and over time subject matter experts within a domain respond to the surveys with the same answers producing the similar results.
Furthermore, the preparation, and interpretation of these surveys can be time consuming. Those who are surveyed generally expect to be monetarily compensated for their time. Moreover, considerable effort may be spent on identifying which persons to send the surveys to. Thus, improved techniques for identifying key entities are needed.
A social network is a structure of nodes which generally represent organizations or individuals but can represent other entities. The nodes are connected to one another by links which represent relationships between entities represented by the nodes. A graphic representation of a social network is called a sociogram.
Social network analysis is a process of generating information from a social network. Using the techniques of social network analysis, information on social prestige and social position can be obtained for entities or groups of entities in a network. Information on the transport of influence and communication in a network can also be harvested. Visualization of sociograms, for instance, assists in determining barriers to the movement of information or paths by which information is likely to diffuse through.
Advanced matrix mathematical methods can be used to obtain numerical variables, called network centrality metrics, which quantitatively indicate characteristics of a node in a network. These characteristics provide insight into the social role of an entity in the network. There are several different types of network centrality metrics, including betweeness, Bonacich's power centrality, closeness, degree, and eigenvector centrality, as well as others. Each provides different information on the status, or importance of an entity in a network. Combinations of values for the different centrality measures, for a particular entity, may be used in combination to assess that entities importance, or role in the network. In addition, non-centrality metric data, for instance a researcher's publication count or an institution's geography may be combined with the centrality metric values to obtain an even clearer picture of the role of an entity in a particular social network.
Recent advances in computer technology, including improvements in the speed at which data may be acquired from remote sources, through the Internet, and the increased ability of a computer to manipulate large amounts of data within a short period of time have made the acquisition, construction, and analysis of large social networks, using network centrality metrics and other procedures, possible. In addition, new text matching software, designed for cleaning of large data sets, allows for the accurate analysis of large social networks from which a wealth of diverse, informative data can be constructed.
Methods for identifying key entities based on network and/or relationship properties have been described previously. For example, U.S. Patent Application Publication 2007/0271272 A1 teaches using connections in a personal-communications network to identify opinion leaders, where connections may be defined by the quantity of times a person's name is searched in a search engine. The method does not, however, teach segmentation of key entities using network centrality measures or the use of reach in combination with network centrality measures to systematically define adequate numbers of key entities in a subgraph of key entities or subgroups of key entities.
US 2006/0184464 A1 teaches finding individuals in organizations that are key individuals using profiles built from analyzing metadata including relationship data from a dataset. US 2006/0184464 A1 does not teach network centrality metrics or reach or segmentation.
US 2004/0073476 A1 teaches a method for obtaining a subgraph of key opinion leaders based upon an automated survey methodology. The method does not teach social network analysis for identifying key entities.
US 2002/0169737 A1 discloses an Internet accessible method for displaying relationships between people, organizations, and articles. This application also teaches the concept of “reach” to assess the connectedness of an entity in a network. It does not teach network centrality metrics or segmentation of key entities by network centrality metrics or the use of reach in combination with network centrality metric values to systematically define adequate numbers of key entities in a subgraph of key entities or subgroups of key entities.
US 2005/0080655 A1 teaches a method of assessing the quality of collaborative relationships, mentioning the use several different possible approaches including social network analysis (SNA). While reach and centrality metrics are mentioned as possible tools to assess quality of relationships, no systematic method for identifying key entities is presented using reach and centrality measures. The reference does not teach segmentation of key entities by network centrality metrics or the use of reach in combination with network centrality metric values to systematically define adequate numbers of key entities in a subgraph of key entities or subgroups of key entities.